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import torch
from torch import Tensor, nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
import math
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2)
* (-math.log(10000.0) / d_model))
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: Tensor, shape [seq_len, batch_size, embedding_dim]
"""
x = x + self.pe[:x.size(0)]
return self.dropout(x)
class TransformerModel(nn.Module):
def __init__(self, ntoken: int, d_model: int, nhead: int, d_hid: int,
nlayers: int, dropout: float = 0.5):
super().__init__()
self.model_type = 'Transformer'
self.pos_encoder = PositionalEncoding(d_model, dropout)
encoder_layers = TransformerEncoderLayer(
d_model, nhead, d_hid, dropout)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.embedding = nn.Embedding(ntoken, d_model)
self.d_model = d_model
self.decoder = nn.Linear(d_model, ntoken)
self.init_weights()
def init_weights(self) -> None:
initrange = 0.1
self.embedding.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:
"""
Args:
src: Tensor, shape [seq_len, batch_size]
src_mask: Tensor, shape [seq_len, seq_len]
Returns:
output Tensor of shape [seq_len, batch_size, ntoken]
"""
src = src.long()
src = self.embedding(src) * math.sqrt(self.d_model)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, src_mask)
output = self.decoder(output)
return output |